One look at a chart can help us draw conclusions. That’s the beauty of data visualization.

In a previous article, I discussed how we use PIE (Player Impact Estimate) within Viziball, a basketball analytics website. This general measure proposes to aggregate statistical outputs in order to obtain a quick summary of how a player influences the game.

Despite all it gives us, this metric has limitations (as with any indicator). The first is that we only have visible game facts and that there are ways to influence a game other than producing stats. The second limitation is that the PIE measure gives a general assessment and that it is, therefore, difficult to trace the affected game aspects.

It’s this second limitation that we try to deal with. In order to provide more elaborate insights, we propose a segmentation of the PIE measure around 8 axes: shooting, scoring, offensive aggressiveness, defensive aggressiveness, go-to guy, catch & score, play-making, and clutchness.